Hidden Markov Modeling of eye movements with image information leads to better discovery of regions of interest
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016 |
Publisher | The Cognitive Science Society |
Pages | 1032-1037 |
ISBN (print) | 9780991196739 |
Publication status | Published - 2016 |
Publication series
Name | Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016 |
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Conference
Title | 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 |
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Place | United States |
City | Philadelphia |
Period | 10 - 13 August 2016 |
Link(s)
Document Link | Links
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85105416826&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(ced05b0a-06d4-4716-9168-42db915c0b12).html |
Abstract
Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved as compared with earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows reconstructing image information at each fixation. © Copyright 2022 Elsevier B.V., All rights reserved.
Research Area(s)
- Eye-tracking, Face Recognition, Hidden Markov Model, Machine Learning
Citation Format(s)
Hidden Markov Modeling of eye movements with image information leads to better discovery of regions of interest. / Brueggemann, Stephan; Chan, Antoni B.; Hsiao, Janet H.
Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. The Cognitive Science Society, 2016. p. 1032-1037 (Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016).
Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. The Cognitive Science Society, 2016. p. 1032-1037 (Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review